Hamiltonian Systems and Chaos Overview
Liz Lane-Harvard, Melissa Swager
Abstract: In this paper we will give an overview of Hamiltonian systems with specific
examples, including the classical pendulum example. Upon addressing the properties of
Hamiltonian systems we will then explore how chaos arises. More specifically, we will consider
two examples: solar system orbits and a zero-sum two-player game.
1. Introduction
Hamiltonian Mechanics is a derivation of classical mechanics offering a deeper insight into the
connection between classic and Lagrangian mechanics. There are many applications, to name a
few: simple harmonic oscillators, planet orbits, and the weather. Perturbations in Hamiltonian
systems give rise to chaos, which can be explained through KAM theory. Although the study of
Hamiltonian Mechanics has been around since the 1800’s, chaos is only now beginning to be
understood.
2. History
Hamiltonian Mechanics was introduced in 1833 by the Irish mathematician William Rowan
Hamilton. As a man inspired with knowledge at a very young age
Hamilton was able to accomplish many tasks in only the 60 years he
lived. He attended Trinity College in Dublin, which is where he would
spend his entire academic career. While there, he studied optics, classical
mechanics, dynamic methods, quaternions, conjugate algebraic couple
functions, and many other topics. Hamilton remained active in the
mathematical society until his death in 1865 from a severe case of gout,
due to excessive drinking [18].
Figure 1: William Hamilton [19]
3. Overview of Hamiltonian Systems
Hamiltonian systems satisfy a number of properties, but before addressing those, an overview of
what a Hamiltonian system is needs to be addressed. A Hamiltonian system with n degrees of
freedom on an open subset E of
must satisfy the following:
Let
where
with
. Then the system
,
satisfies
and
where x represents a generalized coordinate vector and y represents a momentum vector [11].
But why classify these systems? Why not just consider Lagrangian systems? The reason to step
away from Lagrangian systems is that Hamiltonian systems link classical and quantum
mechanics. More specifically, Hamiltonian systems are conservative, which can easily be
extended to many physical systems. Furthermore, the biggest difference between Hamiltonian
systems and Lagrangian systems is the fact that Hamiltonian equations have a larger set of
symmetries. By applying these equations to physical systems, it is easier to exploit and
manipulate the symmetries. Thus, Hamiltonian systems provide more tools to implement when
trying to understand a physical system.
One of the most basic examples of a Hamiltonian system is the ideal pendulum. In this
case, there is a mass m attached to a rigid, massless rod of length L in a “vacuum.” The
momentum of this system is in terms of the angular momentum, namely,
, and the
potential energy is given by
where is the gravitational force inside the vacuum.
These two equations lead to the Hamiltonian system:
where
and
[7].
Figure 2: Ideal Pendulum [10]
In fact, the pendulum is a more specific type of a Hamiltonian system. The class of
Hamiltonian systems in which the pendulum fits into is called the Newtonian system.
Newtonian systems have one degree of freedom and can be written in the form
where
. In turn, this differential equation can be re-written as a system in
where
.
Then the total energy is given by
where
is the kinetic energy and
is the potential energy [11]. And clearly, when rewriting the Newtonian
system in this manner, it can be seen that it is, in fact, a Hamiltonian system. An interesting fact
about Newtonian systems is that all the critical points lie on the x-axis, which is further
supported by the pendulum example [11].
Hamiltonian systems are also related to gradient systems. A gradient system is defined as
follows: Let E be an open subset of
and let
. A system of the form
where
is called a gradient system on E [11]. But
how is this related to Hamiltonian systems? The system of the form
,
(a
planar system) is a Hamiltonian system if and only if the system,
,
,
orthogonal to the planar system is a gradient system [11]. This connection is another tool to be
used when investigating Hamiltonian systems.
3.1 Properties
Hamiltonian systems consist of numerous properties. The most well-known property is that
Hamiltonian systems conserve at least one particular quantity, namely energy. More
specifically, the total energy H of the Hamiltonian system remains constant along its trajectories.
This can be proven by taking the derivative of H with respect to time t. In doing so, we get that
it equals zero, which proves that H is constant along any solution curve. Therefore, the
trajectories of the system are on the surfaces of H, which is a constant. In particular, the ideal
pendulum system conserves energy. It is important to note that there are systems that conserve
energy but are non-Hamiltonian. Thus, conversing energy is not the only key property of
Hamiltonian systems. Furthermore, Hamiltonian systems are frictionless and the equations are
time-reversible. Also, the motion of the system occurs in a hypersurface and the flow is
incompressible. Incompressible flow is characterized by the unchanged density of a system.
Hamiltonian systems also possess other properties, but those will not be addressed in this paper
[1].
Now properties of Hamiltonian systems as they relate to an entry level ordinary
differential equation course will be addressed. In particular, equilibrium points will be
investigated. The first thing to note is that the critical points of H correspond to the equilibrium
points of the system. Furthermore, the equilibrium points are non-degenerate if the determinant
of the second derivative of H is nonzero when evaluated at the equilibrium points. Stability can
also be determined using the second derivative of H. When evaluating the second derivative at
an equilibrium point, if all the eigenvalues have a positive real part, then that point is stable. If
we consider Hamiltonian systems of degree one, then non-degenerate equilibrium points can be
classified more readily. If an equilibrium point is non-degenerate, then we have that that point is
a saddle of the Hamiltonian system if and only if it is a saddle of H. Furthermore, it is a center if
and only if it is a local maximum or a local minimum of H [8]. In the pendulum case, we can
see from the following phase space trajectories that we get centers and saddles.
Figure 3: Ideal Pendulum Phase Portrait [9]
3.2 Examples
In order to see how the above properties apply to Hamiltonian systems, a more complicated
example will be explored. One such example is that of charged particles in a magnetic field.
More specifically, the Kepler problem will be investigated. In the Kepler system there are two
particles in . This system has one particle fixed at the origin, while the other moves based on
the gravitational field created by the fixed particle. What is being investigated in the Kepler
problem is the movement of the non-fixed particle. The Hamiltonian system is as follows:
where <,> is the Euclidean inner product, |q| is the length of the vector q, and u is the attractive
force (u>0). Based on this system, we obtain the following two equations:
,
[16].
This system conserves angular momentum and occurs in many cases. It arises in celestial
mechanics; more specifically, it deals with satellites moving about planets or a planet about the
sun. The Kepler problem also arises in electrostatics, especially with hydrogen atoms,
positronium, and muonium. While the Kepler system is a bit more complicated system than that
of the pendulum, it is the other foundation to classical mechanics (the other being the pendulum).
In fact, in these two cases, they both have closed orbits for every possible set of initial conditions
[4]. The following image is a prototype of the Kepler problem.
Figure 4: An example of a Kepler problem [12]
Another example that is timeless and has numerous applications is the field line behavior.
Consider a vector field
where
. Then the field line behavior in space can be written
as follows:
But this formula can be re-written (and simplified) in parametric form to obtain the following:
where t represents the length of the field line. This parametric form is then used for various
applications. One application is that for advected particles in fluids, also known as tracers. The
following figure is a neat, specific example of where tracers are actually used in the real world.
This example is used to help design video games from Intel by simulating a ball flowing through
a fluid and a fluid flowing past a ball. Another application for field line behavior is in magnetic
field lines with respect to fusion devices [20].
3.3 Integrability versus Non-Integrability
Integrability of a Hamiltonian system plays an enormous role in the perturbation of that system.
In fact, as it will be discussed in the following section, chaos can occur when a system becomes
perturbed. Thus, integrability of a system is a necessary factor when studying Hamiltonian
systems. Furthermore, the definition of integrability is not unique, so this paper will present a
variety of definitions. The reader may choose to use which ever definition suites her best.
Lastly, before presenting these definitions, it is important to realize that these definitions have a
specific name. Specifically, we say a Hamilton system is Liouville integrable if any of the
following definitions is satisfied. One definition of integrability is as follows: a system is
integrable if its solutions can be expressed through quadratures [20]. But the word quadrature
has at least three distinct meanings in mathematics, so this definition is a bit ambiguous.
Therefore, the following is another definition of integrability: a system is integrable if the
number M of independent commuting integrals satisfy the condition
(N is the degrees of
freedom), but the family of trajectories of the Hamiltonian system cannot be displayed on an
invariant N-torus. Or rather, a system is integrable if it can be written in the following form:
,
where I is a function of actions and
are phases canonically conjugated to I [20].
But, these definitions of integrability could be negated to give us the definition of a Hamiltonian
system being non-integrable. We will now consider several non-integrable systems with two
degrees of freedom.
Double Torus: Non-Integrable
2-Dimension Billiard: Non-Integrable
2-Dimension Billiard: Non-Integrable
2-Dimension Billiard: Non-Integrable
These four systems are non-integrable due to the trajectories that wind about each surface. These
trajectories are sensitive to small perturbations. Thus, the systems cannot be integrated.
4. Hamiltonian Chaos
4.1 Definition and Properties
In a non-chaotic Hamiltonian system, the motion is oscillatory. Thus, geometrically we get that
the orbits of the system move on tori. But, for chaos to be introduced into the system, the tori
need to be destroyed. And by destroying the invariant tori, the system in turn creates a cantori.
Chirikov first discovered that for local chaos to occur in a Hamiltonian system, stable and
unstable manifolds had to intersect. And this chaos occurs when
is the frequency and
, where
,
is the distance frequency between two unperturbed resonances.
Furthermore, Chirokov found that global chaos occurs when
[15]. Poincaré labeled
this occurrence “homoclinic trellis.” This idea can be seen through the work of John Greene in
1968. His work dealt with further perturbing a KAM torus. He found that if one continually
perturbs a KAM torus, it reaches a point where the phase space has fractal structure. Upon
reaching this point, if the torus is perturbed anymore, the torus will be destroyed, creating a
cantorus [7].
But the question should be, how do these tori become perturbed? It should be noted that
the tori are not perturbed in the integrable Hamiltonian system H that was defined earlier.
Rather, the system becomes perturbed when a nonintegrable Hamiltonian perturbation is added.
In doing so, the following equation is obtained:
.
In adding the nonintegrable function, tori begin to deform, and those that survive are
“sufficiently irrational.” In fact, according to KAM theory, tori survive for
perturbations if
for
and is of order
for small
, where
is based
on frequency. But for chaos to occur, even these “sufficiently irrational” tori become perturbed
[1].
4.2 Chaos Examples
One common and interesting example of Hamiltonian chaos is the problem of stability of the
solar system. Is the solar system stable over long periods of time? This can be modeled as a
Hamiltonian system by considering the force between each pair of massive bodies, or planets.
The problem is simple to set up but very difficult to solve, so difficult, in fact, that it hasn’t been
answered and continues to stump mathematicians. So, mathematicians considered a more simple
system using only the sun and two planets. This has become famously known as the “three body
problem”. In the mid-20th Century three Russian mathematicians found the solution, known
today as the KAM theorem. The theorem is based on invariant tori and answers the question,
what happens to the invariant tori as the nonlinearity of the system increases? How does this
answer our solar system question? Well, the planets motion represent that of a torus, so over a
long period of time if we know what happens to a torus then we know what happens to the solar
system. The KAM theorem concludes
that tori go unstable in order of their
degree of irrationality. So, for the
“three body problem” consider the
Earth, Jupiter, and the Sun. The full
phase space would be 18 dimensional
and the motion is represented as a
torus. The winding numbers is the
same as the ratio of orbit periods
which happens to be about 11.862972.
This seems fairly irrational, so we
could expect Earth’s orbit to be fairly
stable. This is of course a simple
model and becomes more difficult
when considering larger and more
complicated systems [6]. But this
theory tells us how Hamiltonian
Figure 5: Solar system and chaos [16]
systems become chaotic, which is what we’re interested in.
Other interesting examples arise in
economics and simple two-person games.
Consider the zero-sum game of rock, paper,
scissors. The zero-sum game can be represented
as a Hamiltonian system since the learning
dynamics have a conserved quantity. We would
expect the game to converge to the Nash
Equilibrium, since this would imply that at every
step of the game, a player tries to better his/her
position in the game. However, this is not always
true and when a player does not take the Nash
Equilibrium mixed strategy, which is to choose
the move randomly with all choices having
Figure 6: Rules of Rock, Paper, Scissors [13]
equal probability, the game results in chaos.
This can be modeled by:
Player one chooses from different strategies with frequency
chooses from different strategies with frequency
for player one and B is the payoff matrix for player two.
and player two
, where A is the payoff matrix
,
Note that
and
are the payoffs for a tie. Since
, the dynamics are
Hamiltonian. Choosing
with
and varying from 0 to 0.5 results in chaos [14]. Nash strategy appears often in
economics and has similar outcomes to the rock, paper, scissors game [14].
5. Conclusion
There is still much to be known about chaos, as seen in the example of the solar system. The
neat thing about these systems is that they apply to many areas. We would be interested in
seeing if Hamiltonian chaos arises in an n-person (
) zero-sum game. And we also look
forward to the discoveries that will be made in the study of chaos with regards to the solar
system. This paper is just a brief overview of Hamiltonian chaos and the interested reader should
seek further references.
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